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Biological meaning, statistical significance, and classification of local spatial similarities in nonhomologous proteins.

ABSTRACT: We have completed an exhaustive search for the common spatial arrangements of backbone fragments (SARFs) in nonhomologous proteins. This type of local structural similarity, incorporating short fragments of backbone atoms, arranged not necessarily in the same order along the polypeptide chain, appears to be important for protein function and stability. To estimate the statistical significance of the similarities, we have introduced a similarity score. We present several locally similar structures, with a large similarity score, which have not yet been reported. On the basis of the results of pairwise comparison, we have performed hierarchical cluster analysis of protein structures. Our analysis is not limited by comparison of single chains but also includes complex molecules consisting of several subunits. The SARFs with backbone fragments from different polypeptide chains provide a stable interaction between subunits in protein molecules. In many cases the active site of enzyme is located at the same position relative to the common SARFs, implying a function of the certain SARFs as a universal interface of the protein-substrate interaction.

SUBMITTER: Alexandrov NN

PROVIDER: S-EPMC2142880 | biostudies-other | 1994 Jun

REPOSITORIES: biostudies-other

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Biological meaning, statistical significance, and classification of local spatial similarities in nonhomologous proteins.

Alexandrov N N NN Go N N

Protein science : a publication of the Protein Society 19940601 6

We have completed an exhaustive search for the common spatial arrangements of backbone fragments (SARFs) in nonhomologous proteins. This type of local structural similarity, incorporating short fragments of backbone atoms, arranged not necessarily in the same order along the polypeptide chain, appears to be important for protein function and stability. To estimate the statistical significance of the similarities, we have introduced a similarity score. We present several locally similar structure ...[more]

PMID: 8069217

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Project description:In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties. However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method. This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes. Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder. We analyzed the impact of the five scales on classification accuracy. In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared. Finally, traditional statistical significance (P value) was verified as a feature selection criterion. The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features. Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification. However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater. The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P < 0.05 threshold was too strict for feature selection. This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.

Dataset Information

Biological meaning, statistical significance, and classification of local spatial similarities in nonhomologous proteins.

Publications

Biological meaning, statistical significance, and classification of local spatial similarities in nonhomologous proteins.

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